Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models

doi:10.1016/j.scitotenv.2019.06.516

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 山区发展研究中心

	Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models
	Xie, Xinyao1,2 ; Li, Ainong1 ; Jin, Huaan1 ; Tan, Jianbo3 ; Wang, Changbo 1,2; Lei, Guangbin1 ; Zhang, Zhengjian1,2 ; Bian, Jinhu1 ; Nan, Xi1
刊名	SCIENCE OF THE TOTAL ENVIRONMENT
	2019-11-10
卷号	690 页码:1120-1130
关键词	Satellite-derived LAI datasets Gross primary productivity Topographic effects LUE model Process-based model
ISSN号	0048-9697
DOI	10.1016/j.scitotenv.2019.06.516
通讯作者	Li, Ainong(ainongli@imde.ac.cn)
英文摘要	Ecosystem models have been widely used for obtaining gross primary productivity (GPP) estimations at multiple scales. Leaf area index (LAI) is a critical variable in these models for describing the vegetation canopy structure and predicting vegetation-atmosphere interactions. However, the uncertainties in LAI datasets and the effects of their representation on simulated GPP remain unclear, especially over complex terrain. Here, five most popular datasets, namely the Long-term Global Mapping (GLOBMAP) LAI, Global LAnd Surface Satellite (GLASS) LAI, Geoland2 version 1 (GEOV1) LAI, Global Inventory Monitoring and Modeling System (GIMMS) LAl,and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI, were selected to examine the influences of LAI representation on GPP estimations at 95 eddy covariance (EC) sites. The GPP estimations from the Boreal Ecosystem Productivity Simulator (BEPS) model and the Eddy Covariance Light Use Efficiency (EC-LUE) model were evaluated against EC GPP to assess the performances of LAI datasets. Results showed that MODIS LAI had stronger linear correlations with GLASS and GEOV1 than GIMMS and GLOMAP at the study sites. The GPP estimations from GLASS LAI had a better agreement with EC GPP than those from other four LAI datasets at forest sites, while the GPP estimations from GEOVI LAI matched best with EC GPP at grass sites. Additionally, the GPP estimations from GLASS and GEOVI LAI presented better performances than the other three LAI datasets at crop sites. Besides, the results also showed that complex terrain had larger discrepancies of LAI and GPP estimations, and flat terrain presented better performances of LAI datasets in GPP estimations. Moreover, the simulated GPP from BEPS was more sensitive to LAI than those from EC - LUE, suggesting that LAI datasets can also lead to different uncertainties in GPP estimations from different model structures. Our study highlights that the satellite-derived LAI datasets can cause uncertainties in GPP estimations through ecosystem models. (C) 2019 Published by Elsevier B.V.
资助项目	National Key Research and Development Program of China[2016YFA0600103] ; National Natural Science Foundation of China[41631180] ; National Natural Science Foundation of China[41571373] ; National Natural Science Foundation of China[41671376] ; National Natural Science Foundation of China[41701433] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030303] ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS[SDS-135-1708]
WOS关键词	LEAF-AREA INDEX ; GROSS PRIMARY PRODUCTION ; ESSENTIAL CLIMATE VARIABLES ; CARBON-DIOXIDE ; GEOV1 LAI ; MODIS ; PRODUCTIVITY ; VARIABILITY ; VALIDATION ; TOPOGRAPHY
WOS研究方向	Environmental Sciences & Ecology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000482549900101
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/26950]
专题	成都山地灾害与环境研究所_山区发展研究中心成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者	Li, Ainong
作者单位	1.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Sichuan, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410114, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Xie, Xinyao,Li, Ainong,Jin, Huaan,et al. Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2019,690:1120-1130.
APA	Xie, Xinyao.,Li, Ainong.,Jin, Huaan.,Tan, Jianbo.,Wang, Changbo.,...&Nan, Xi.(2019).Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models.SCIENCE OF THE TOTAL ENVIRONMENT,690,1120-1130.
MLA	Xie, Xinyao,et al."Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models".SCIENCE OF THE TOTAL ENVIRONMENT 690(2019):1120-1130.